Who should use the Monitor Data Quality workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
A streamlined workflow to validate, prepare, monitor, and manage data quality using specialized tasks and tools for reliable insights.
Deliverable outcome
An evolving, stakeholder-aligned quality framework that stays relevant.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
An evolving, stakeholder-aligned quality framework that stays relevant.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use DQLabs to a clear, documented set of quality metrics and thresholds to guide all subsequent monitoring. Then, you pass the output to dbt Cloud (AI-Powered) to a baseline quality report showing current data health and immediate violations. Then, you pass the output to Soda AI to continuous, automated validation with real-time alerts for quality degradation. Then, you pass the output to Tableau AI to a live dashboard showing quality trends over time, enabling proactive detection of drift. Then, you pass the output to Atlan to resolved quality issues with documented root cause and corrective actions. Finally, Atlan is used to an evolving, stakeholder-aligned quality framework that stays relevant.
Define Data Quality Metrics and Thresholds
A clear, documented set of quality metrics and thresholds to guide all subsequent monitoring.
Profile Source Data
A baseline quality report showing current data health and immediate violations.
Implement Automated Validation Checks
Continuous, automated validation with real-time alerts for quality degradation.
Monitor and Visualize Quality Trends
A live dashboard showing quality trends over time, enabling proactive detection of drift.
Investigate and Remediate Quality Issues
Resolved quality issues with documented root cause and corrective actions.
Review and Update Quality Rules Periodically
An evolving, stakeholder-aligned quality framework that stays relevant.
Identify key dimensions of data quality relevant to your domain (e.g., completeness, accuracy, consistency, timeliness). For each dimension, define measurable thresholds (e.g., 'missing rate < 5%') and acceptable ranges. Document these in a shared data quality specification.
Why DQLabs: DQLabs provides a comprehensive platform for defining and enforcing data quality rules, which directly matches the need for a data quality framework to set metrics and thresholds.
Run automated profiling on the raw data sources to understand current state: column statistics, null counts, data types, distribution patterns. Compare profiling results against the defined thresholds to identify immediate issues.
Why dbt Cloud (AI-Powered): dbt Cloud (AI-Powered) includes automated SQL generation and profiling capabilities, making it a strong fit for profiling source data.
Deploy automated validation rules (e.g., Great Expectations expectations, dbt tests) that run on data ingestion or transformation. Configure alerts (email, Slack) for threshold breaches. Schedule these checks to run on a recurring basis (hourly/daily).
Why Soda AI: Soda AI specializes in data quality monitoring and anomaly detection, directly aligning with the need for an automated validation framework.
Aggregate validation results over time into a dashboard (e.g., Grafana, Metabase, or custom BI). Track trends for each metric (e.g., null rate over last 30 days). Set up weekly or monthly review cadence to spot gradual degradation.
Why Tableau AI: Tableau AI is a dedicated data visualization and dashboarding tool, ideal for monitoring and visualizing quality trends over time.
When alerts or trend anomalies appear, drill down into affected records using SQL or profiling tools. Identify root cause (e.g., upstream source change, bug in transformation, human error). Apply fixes (e.g., update pipeline logic, re-run transformation, notify data owner).
Why Atlan: Atlan offers data discovery and lineage tracking, which is essential for investigating the root cause of quality issues and understanding data flow.
Every quarter (or after major pipeline changes), review the quality metrics and thresholds with stakeholders. Add new rules for emerging data sources or business requirements. Retire obsolete checks to reduce noise.
Why Atlan: Atlan supports data governance and cataloging, which facilitates collaboration and documentation needed for periodic review and updating of quality rules.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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